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App analytics lead (mantova)

Mantova
Prada
Pubblicato il Pubblicato 17h fa
Descrizione

JOB PURPOSE

For the Digital Team based in Milan HQ, we are looking for the App Analytics Lead, who will be responsible for the mobile apps performance analytics across B2C and B2B Group App Ecosystem, driving insight generation to optimize engagement, retention, and business impact. We offer a stimulating work environment, with multiple innovative projects and a network of colleagues with different skill sets and backgrounds (UX designer, Product Owners, Data Analysts, E-commerce, Media).

RESPONSIBILITIES

- Own and lead app performance measurement for B2C and B2B mobile experiences, ensuring clarity, consistency, and business relevance of KPIs

- Design, maintain, and evolve dashboards and reporting frameworks to monitor acquisition, engagement, retention, and conversion performance

- Conduct deep-dive analyses to identify behavioral patterns, performance drivers, and optimization opportunities across different user segments

- Partner with Product, UX, Marketing, CRM, and Tech teams to translate data into actionable recommendations

- Lead and support A/B testing and experimentation strategies to improve user experience and business outcomes

- Ensure best-in-class implementation and governance of mobile analytics tools (e.g. SDKs, event tracking, attribution)

- Define and promote analytics best practices across teams, fostering a strong data-driven culture

- Act as a trusted analytics partner for stakeholders, adapting insights and storytelling to both B2C and B2B audiences

KNOWLEDGE AND SKILLS

- 6+ Proven experience in mobile app analytics, with hands‑on expertise in industry‑standard tools (e.g. Firebase, Adobe Analytics, Adjust, AppsFlyer, Amplitude or similar)

- Strong background in KPI definition, performance monitoring, and insight generation

- Solid experience with dashboarding and data visualization tools (e.g. Power BI, Looker, Tableau, QlikSense)

- Demonstrated ability to work across both B2C and B2B business models, understanding their di

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